Domain-Aware Dialogue State Tracker for Multi-Domain Dialogue Systems
Abstract
In task-oriented dialogue systems the dialogue state tracker (DST) component is responsible for predicting the state of the dialogue based on the dialogue history. Current DST approaches rely on a predefined domain ontology, a fact that limits their effective usage for large scale conversational agents, where the DST constantly needs to be interfaced with ever-increasing services and APIs. Focused towards overcoming this drawback, we propose a domain-aware dialogue state tracker, that is completely data-driven and it is modeled to predict for dynamic service schemas. The proposed model utilizes domain and slot information to extract both domain and slot specific representations for a given dialogue, and then uses such representations to predict the values of the corresponding slot. Integrating this mechanism with a pretrained language model (i.e. BERT), our approach can effectively learn semantic relations.
Cite
@article{arxiv.2001.07526,
title = {Domain-Aware Dialogue State Tracker for Multi-Domain Dialogue Systems},
author = {Vevake Balaraman and Bernardo Magnini},
journal= {arXiv preprint arXiv:2001.07526},
year = {2020}
}